Performance Analysis of Decision Tree Algorithms for Breast Cancer Classification

Backgrounds/Objectives: Data Mining (DM) techniques are extremely utilized for the extraction of useful information which is available in data warehouses and other database repositories. In medical diagnose, the role of DM approach rises quick recognition of disease over symptoms. To classify the medical data, a number of DM techniques are used by researchers. One of such techniques is classification. The classification algorithms predict the hidden information in the medical domain. The breast cancer is the very dangerous disease for women in developed countries like India. Most of the women death happens in the world, they are affected by the breast cancer. Methods/Statistical Analysis: The role of classification is importantin the real world applications in every field. Classification is used to classify the elements permitting to the features of the elements through the predefined set of classes. This research work analyses the breast cancer data using classification algorithms namely j48, Classification and Regression Trees (CART), Alternating Decision Tree (AD Tree) and Best First Tree (BF Tree). Findings: To find the performance of classification algorithms, this work uses cancer data as input. Particularly, this work is carried out to compare the four decision tree algorithms in the prediction of the performance accuracy in breast cancer data. All the algorithms are applied for breast cancer data to classify the data set for classification and prediction. Among these four methods, this work concludes the best algorithm for the chosen input data on decision tree supervised learning algorithms to predict the best classifier. Applications/Improvements: The breast cancer data is analyzed by taking the images using the same algorithms in future. Also, the microcalcifications of the breast cancer imagery are to be investigated in the same work. Keywords: CART Algorithm, Classification Algorithms, Decision Trees, J48 Algorithm